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Updated: May 27, 2025
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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xRead: a coverage-guided approach for scalable construction of read overlapping graph.
Tangchao Kong1,2, Yadong Wang1,2, Bo Liu1,2
1Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
Gigascience
|February 17, 2025
Summary
xRead efficiently constructs overlapping graphs for large genomes, overcoming bottlenecks in de novo sequencing. This novel approach enables high-performance, scalable genome assembly even with massive datasets and limited RAM.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Long-read sequencing enables high-quality de novo genome assembly.
- Current assemblers struggle with large datasets, limiting scalability.
- Read overlapping graph construction is a major performance bottleneck.
Purpose of the Study:
- To develop a high-performance and scalable approach for overlapping graph construction.
- To address the limitations of existing tools in handling large-scale de novo sequencing.
Main Methods:
- Introduced xRead, an iterative overlapping graph construction method.
- Utilized a coverage-based model to convert read-overlapping to heuristic read-mapping.
- Implemented incremental graph construction with controlled RAM usage.
Main Results:
- xRead processes large datasets (e.g., 1.28 Tb) using <64 GB RAM.
- Achieved significantly reduced time costs compared to existing tools.
- Generated accurate and well-connected overlapping graphs.
Conclusions:
- xRead overcomes critical bottlenecks in genome graph construction.
- Provides a foundation for efficient large-scale de novo assembly.
- Suitable for numerous large-genome datasets in sequencing studies.
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